Adiposity, metabolites, and colorectal cancer risk: Mendelian randomization study.

BACKGROUND: Higher adiposity increases the risk of colorectal cancer (CRC), but whether this relationship varies by anatomical sub-site or by sex is unclear. Further, the metabolic alterations mediating the effects of adiposity on CRC are not fully understood. METHODS: We examined sex- and site-specific associations of adiposity with CRC risk and whether adiposity-associated metabolites explain the associations of adiposity with CRC. Genetic variants from genome-wide association studies of body mass index (BMI) and waist-to-hip ratio (WHR, unadjusted for BMI; N = 806,810), and 123 metabolites from targeted nuclear magnetic resonance metabolomics (N = 24,925), were used as instruments. Sex-combined and sex-specific Mendelian randomization (MR) was conducted for BMI and WHR with CRC risk (58,221 cases and 67,694 controls in the Genetics and Epidemiology of Colorectal Cancer Consortium, Colorectal Cancer Transdisciplinary Study, and Colon Cancer Family Registry). Sex-combined MR was conducted for BMI and WHR with metabolites, for metabolites with CRC, and for BMI and WHR with CRC adjusted for metabolite classes in multivariable models. RESULTS: In sex-specific MR analyses, higher BMI (per 4.2 kg/m2) was associated with 1.23 (95% confidence interval (CI) = 1.08, 1.38) times higher CRC odds among men (inverse-variance-weighted (IVW) model); among women, higher BMI (per 5.2 kg/m2) was associated with 1.09 (95% CI = 0.97, 1.22) times higher CRC odds. WHR (per 0.07 higher) was more strongly associated with CRC risk among women (IVW OR = 1.25, 95% CI = 1.08, 1.43) than men (IVW OR = 1.05, 95% CI = 0.81, 1.36). BMI or WHR was associated with 104/123 metabolites at false discovery rate-corrected P ≤ 0.05; several metabolites were associated with CRC, but not in directions that were consistent with the mediation of positive adiposity-CRC relations. In multivariable MR analyses, associations of BMI and WHR with CRC were not attenuated following adjustment for representative metabolite classes, e.g., the univariable IVW OR for BMI with CRC was 1.12 (95% CI = 1.00, 1.26), and this became 1.11 (95% CI = 0.99, 1.26) when adjusting for cholesterol in low-density lipoprotein particles. CONCLUSIONS: Our results suggest that higher BMI more greatly raises CRC risk among men, whereas higher WHR more greatly raises CRC risk among women. Adiposity was associated with numerous metabolic alterations, but none of these explained associations between adiposity and CRC. More detailed metabolomic measures are likely needed to clarify the mechanistic pathways.

Determining Risk of Colorectal Cancer and Starting Age of Screening Based on Lifestyle, Environmental, and Genetic Factors.

BACKGROUND & AIMS: Guidelines for initiating colorectal cancer (CRC) screening are based on family history but do not consider lifestyle, environmental, or genetic risk factors. We developed models to determine risk of CRC, based on lifestyle and environmental factors and genetic variants, and to identify an optimal age to begin screening. METHODS: We collected data from 9748 CRC cases and 10,590 controls in the Genetics and Epidemiology of Colorectal Cancer Consortium and the Colorectal Transdisciplinary study, from 1992 through 2005. Half of the participants were used to develop the risk determination model and the other half were used to evaluate the discriminatory accuracy (validation set). Models of CRC risk were created based on family history, 19 lifestyle and environmental factors (E-score), and 63 CRC-associated single-nucleotide polymorphisms identified in genome-wide association studies (G-score). We evaluated the discriminatory accuracy of the models by calculating area under the receiver operating characteristic curve values, adjusting for study, age, and endoscopy history for the validation set. We used the models to project the 10-year absolute risk of CRC for a given risk profile and recommend ages to begin screening in comparison to CRC risk for an average individual at 50 years of age, using external population incidence rates for non-Hispanic whites from the Surveillance, Epidemiology, and End Results program registry. RESULTS: In our models, E-score and G-score each determined risk of CRC with greater accuracy than family history. A model that combined both scores and family history estimated CRC risk with an area under the receiver operating characteristic curve value of 0.63 (95% confidence interval, 0.62-0.64) for men and 0.62 (95% confidence interval, 0.61-0.63) for women; area under the receiver operating characteristic curve values based on only family history ranged from 0.53 to 0.54 and those based only E-score or G-score ranged from 0.59 to 0.60. Although screening is recommended to begin at age 50 years for individuals with no family history of CRC, starting ages calculated based on combined E-score and G-score differed by 12 years for men and 14 for women, for individuals with the highest vs the lowest 10% of risk. CONCLUSIONS: We used data from 2 large international consortia to develop CRC risk calculation models that included genetic and environmental factors along with family history. These determine risk of CRC and starting ages for screening with greater accuracy than the family history only model, which is based on the current screening guideline. These scoring systems might serve as a first step toward developing individualized CRC prevention strategies.

Spatiotemporal dissection of the Golgi apparatus and the ER-Golgi intermediate compartment in budding yeast.

Cargo traffic through the Golgi apparatus is mediated by cisternal maturation, but it remains largely unclear how the cis-cisternae, the earliest Golgi sub-compartment, is generated and how the Golgi matures into the trans-Golgi network (TGN). Here, we use high-speed and high-resolution confocal microscopy to analyze the spatiotemporal dynamics of a diverse set of proteins that reside in and around the Golgi in budding yeast. We find many mobile punctate structures that harbor yeast counterparts of mammalian endoplasmic reticulum (ER)-Golgi intermediate compartment (ERGIC) proteins, which we term 'yeast ERGIC'. It occasionally exhibits approach and contact behavior toward the ER exit sites and gradually matures into the cis-Golgi. Upon treatment with the Golgi-disrupting agent brefeldin A, the ERGIC proteins form larger aggregates corresponding to the Golgi entry core compartment in plants, while cis- and medial-Golgi proteins are absorbed into the ER. We further analyze the dynamics of several late Golgi proteins to better understand the Golgi-TGN transition. Together with our previous studies, we demonstrate a detailed spatiotemporal profile of the entire cisternal maturation process from the ERGIC to the Golgi and further to the TGN.

Making progress.

Mapping proteins in and associated with the Golgi apparatus reveals how this cellular compartment emerges in budding yeast and progresses over time.

Identifying metabolic features of colorectal cancer liability using Mendelian randomization.

Background: Recognizing the early signs of cancer risk is vital for informing prevention, early detection, and survival. Methods: To investigate whether changes in circulating metabolites characterise the early stages of colorectal cancer (CRC) development, we examined associations between a genetic risk score (GRS) associated with CRC liability (72 single nucleotide polymorphisms) and 231 circulating metabolites measured by nuclear magnetic resonance spectroscopy in the Avon Longitudinal Study of Parents and Children (N=6,221). Linear regression models were applied to examine associations between genetic liability to colorectal cancer and circulating metabolites measured in the same individuals at age 8, 16, 18 and 25 years. Results: The GRS for CRC was associated with up to 28% of the circulating metabolites at FDR-P<0.05 across all time points, particularly with higher fatty acids and very-low- and low-density lipoprotein subclass lipids. Two-sample reverse Mendelian randomization (MR) analyses investigating CRC liability (52,775 cases, 45,940 controls) and metabolites measured in a random subset of UK Biobank participants (N=118,466, median age 58y) revealed broadly consistent effect estimates with the GRS analysis. In conventional (forward) MR analyses, genetically predicted polyunsaturated fatty acid concentrations were most strongly associated with higher CRC risk. Conclusions: These analyses suggest that higher genetic liability to CRC can cause early alterations in systemic metabolism, and suggest that fatty acids may play an important role in CRC development. Funding: This work was supported by the Elizabeth Blackwell Institute for Health Research, University of Bristol, the Wellcome Trust, the Medical Research Council, Diabetes UK, the University of Bristol NIHR Biomedical Research Centre, and Cancer Research UK. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. This work used the computational facilities of the Advanced Computing Research Centre, University of Bristol - http://www.bristol.ac.uk/acrc/.

openEHR-to-FHIR: Converting openEHR Compositions to Fast Healthcare Interoperability Resources (FHIR) for the German Corona Consensus Dataset (GECCO).

The German Corona Consensus (GECCO) established a uniform dataset in FHIR format for exchanging and sharing interoperable COVID-19 patient specific data between health information systems (HIS) for universities. For sharing the COVID-19 information with other locations that use openEHR, the data are to be converted in FHIR format. In this paper, we introduce our solution through a web-tool named "openEHR-to-FHIR" that converts compositions from an openEHR repository and stores in their respective GECCO FHIR profiles. The tool provides a REST web service for ad hoc conversion of openEHR compositions to FHIR profiles.

The Golgi Apparatus and its Next-Door Neighbors.

The Golgi apparatus represents a central compartment of membrane traffic. Its apparent architecture, however, differs considerably among species, from unstacked and scattered cisternae in the budding yeast Saccharomyces cerevisiae to beautiful ministacks in plants and further to gigantic ribbon structures typically seen in mammals. Considering the well-conserved functions of the Golgi, its fundamental structure must have been optimized despite seemingly different architectures. In addition to the core layers of cisternae, the Golgi is usually accompanied by next-door compartments on its cis and trans sides. The trans-Golgi network (TGN) can be now considered as a compartment independent from the Golgi stack. On the cis side, the intermediate compartment between the ER and the Golgi (ERGIC) has been known in mammalian cells, and its functional equivalent is now suggested for yeast and plant cells. High-resolution live imaging is extremely powerful for elucidating the dynamics of these compartments and has revealed amazing similarities in their behaviors, indicating common mechanisms conserved along the long course of evolution. From these new findings, I would like to propose reconsideration of compartments and suggest a new concept to describe their roles comprehensively around the Golgi and in the post-Golgi trafficking.

Gene Coexpression Connectivity Predicts Gene Targets Underlying High Ionic-Liquid Tolerance in Yarrowia lipolytica.

Microbial tolerance to organic solvents such as ionic liquids (ILs) is a robust phenotype beneficial for novel biotransformation. While most microbes become inhibited in 1% to 5% (vol/vol) IL (e.g., 1-ethyl-3-methylimidazolium acetate), we engineered a robust Yarrowia lipolytica strain (YlCW001) that tolerates a record high of 18% (vol/vol) IL via adaptive laboratory evolution. Yet, genotypes conferring high IL tolerance in YlCW001 remain to be discovered. In this study, we shed light on the underlying cellular processes that enable robust Y. lipolytica to thrive in inhibitory ILs. By using dynamic transcriptome sequencing (RNA-Seq) data, we introduced Gene Coexpression Connectivity (GeCCo) as a metric to discover genotypes conferring desirable phenotypes that might not be found by the conventional differential expression (DE) approaches. GeCCo selects genes based on their number of coexpressed genes in a subnetwork of upregulated genes by the target phenotype. We experimentally validated GeCCo by reverse engineering a high-IL-tolerance phenotype in wild-type Y. lipolytica. We found that gene targets selected by both DE and GeCCo exhibited the best statistical chance at increasing IL tolerance when individually overexpressed. Remarkably, the best combination of dual-overexpression genes was genes selected by GeCCo alone. This nonintuitive combination of genes, BRN1 and OYE2, is involved in guiding/regulating mitotic cell division, chromatin segregation/condensation, microtubule and cytoskeletal organization, and Golgi vesicle transport. IMPORTANCE Cellular robustness to cope with stressors is an important phenotype. Y. lipolytica is an industrial robust oleaginous yeast that has recently been discovered to tolerate record high concentrations of ILs, beneficial for novel biotransformation in organic solvents. However, genotypes that link to IL tolerance in Y. lipolytica are largely unknown. Due to the complex IL-tolerant phenotype, conventional gene discovery and validation based on differential gene expression approaches are time-consuming due to a large search space and might encounter a high false-discovery rate. Here, using the developed Gene Coexpression Connectivity (GeCCo) method, we identified and validated a subset of most promising gene targets conferring the IL-tolerant phenotypes and shed light on their potential mechanisms. We anticipate GeCCo being a useful method to discover the genotype-to-phenotype link.